The present invention relates to multi-dimensional queries, and more specifically to multi-dimensional query based extraction of polarity-aware content from documents.
Citation of relevant contents is common in online social networks such as blogs, discussion forums, scientific articles and other written documents. Content recommendation to a user based on a search is often not useful or irrelevant as the existing search systems do not consider simultaneously the context and the polarity or sentiment of the content.
For example, if one were to consider three well known papers in the domain of “viral marketing through social networks”: Paper 1, Paper 2, and Paper 3 all regarding a same topic, for example “Greedy Algorithm for viral marketing”, each paper may present different context and polarity relative to the topic of the paper. For example, the version of the greedy algorithm presented in Paper 1 is highly inefficient in terms of running time; whereas the versions of the greedy algorithm presented in Paper 2 and Paper 3 are highly efficient.
In one example, a user may be writing a blog post (or a survey article) on the use of greedy algorithm for viral marketing purposes and is searching for articles in which to cite or base their post on. Based on the user's query using a prior art system, all three papers, Paper 1, Paper 2, and Paper 3, would be recommended, as the system cannot distinguish between Paper 1 with a negative polarity based on the context of the article (e.g. inefficiency in terms of running time of the greedy algorithm) and Papers 2 and 3 with a positive polarity based on context (e.g. high efficiency in running the greedy algorithm).